Detecting and estimating illuminant colours are important tasks with applications in recognition and classification based on photometric invariants [46], white balancing [23], colour correction, digital media production and graphics [30]. Despite its importance, the recovery and identification of illuminant colours in a scene has proven to be difficult task in uncontrolled real world scenes. This is mainly due to the fact that the recovery of region-wise illumination from a single image is an under-constrained problem [3]. As a result, existing methods often assume a uniform illumination power spectrum throughout the scene [24, 28].
FIG. 1 illustrates an example scene 100 comprising a mountain 102 illuminated by the sun 104 and by light scattered from the atmosphere 106. A first part 110 of mountain 102 is illuminated only by the sun 104, while a second part 112 of the mountain 102 is illuminated only by the atmosphere 106. Each of the two light sources sun 104 and atmosphere 106 has a different illumination spectrum.
When capturing scene 100, conventional cameras assume that there is only a single illumination source, such as the sun 104, for example. As a result, the image appears natural for the first part 110 of mountain 102 but unnatural for second part 112 due to an incorrect white balance, for example.
FIG. 2 illustrates the example scene 100 in more detail. Each of the illuminants sun 104 and atmosphere 106 has a respective illuminant spectrum 204 and 206. The mountain 102 has a reflectance spectrum 210. For simplicity only one reflectance spectrum is shown but of course, many different reflectance spectra of many different materials may be present.
When the light from the illuminants 104 and 106 hits the mountain 102, the illuminant spectra 204 and 206 are multiplied by the reflectance spectrum 210. The resulting spectra are superimposed and reach a sensor 212 as a radiance spectrum 214. The sensor 212 has a number of pixels, such as one million, and captures for each pixel location a separate sampled version of the radiance spectrum.
FIG. 3 illustrates a transformation 300 of first and second pixel radiance spectra 310 and 320 respectively, into a spectral space 330. The first pixel radiance spectrum 310 is sampled at two wavelengths λ1 and λ2. This results in radiance values 311 and 312. The radiance values 311 and 312 of the first pixel are represented by a first sample 331 in the two-dimensional spectral space 330.
Similarly, the second pixel radiance spectrum 320 is sampled at the same two wavelengths λ1 and λ2 resulting in radiance values 321 and 322, which are represented by a second sample 332 in the spectral space 330. In this way, the radiance spectra of many pixels can be represented in the same spectral space 330.
It is noted that in most applications the radiance spectra are sampled at far more points, such as one hundred. In fact, the sample wavelengths may be the same as the wavelengths of the hyperspectral image data. As a result, the sample space 330 is high-dimensional—one dimension for each wavelength.
Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.